AIMC Topic: Diagnostic Imaging

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MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.

BMC medical informatics and decision making
BACKGROUND: The need for better AI models fuels the demand for larger and larger high-quality datasets with significant diversity. Over the years, many medical imaging datasets have been published globally, but existing datasets do not contain enough...

Deep neural hashing for content-based medical image retrieval: A survey.

Computers in biology and medicine
The ever-growing digital repositories of medical data provide opportunities for advanced healthcare by forming a foundation for a digital healthcare ecosystem. Such an ecosystem facilitates digitized solutions to aspects like early diagnosis, evidenc...

Enhanced security for medical images using a new 5D hyper chaotic map and deep learning based segmentation.

Scientific reports
Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New ...

Radio DINO: A foundation model for advanced radiomics and AI-driven medical imaging analysis.

Computers in biology and medicine
Radiomics is transforming medical imaging by extracting complex features that enhance disease diagnosis, prognosis, and treatment evaluation. However, traditional approaches face significant challenges, such as the need for manual feature engineering...

Deep generative models for Bayesian inference on high-rate sensor data: applications in automotive radar and medical imaging.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Deep generative models (DGMs) have been studied and developed primarily in the context of natural images and computer vision. This has spurred the development of (Bayesian) methods that use these generative models for inverse problems in image restor...

Interactive prototype learning and self-learning for few-shot medical image segmentation.

Artificial intelligence in medicine
Few-shot learning alleviates the heavy dependence of medical image segmentation on large-scale labeled data, but it shows strong performance gaps when dealing with new tasks compared with traditional deep learning. Existing methods mainly learn the c...

Implicit neural representation for medical image reconstruction.

Physics in medicine and biology
Medical image reconstruction aims to generate high-quality images from incompletely sampled raw sensor data, which poses an ill-posed inverse problem. Traditional iterative reconstruction methods rely on prior information to empirically construct reg...

Climate-Smart Diagnostic Medical Imaging and Point-of-Care Ultrasound: An Evidence-Based Perspective.

The Journal of emergency medicine
BACKGROUND: Hospital diagnostic imaging significantly contributes to healthcare's carbon emissions, with modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), accounting for a disproportionate share of energy consumption a...

De-identification of medical imaging data: a comprehensive tool for ensuring patient privacy.

European radiology
OBJECTIVES: Medical imaging data employed in research frequently comprises sensitive Protected Health Information (PHI) and Personal Identifiable Information (PII), which is subject to rigorous legal frameworks such as the General Data Protection Reg...

Gender and Ethnicity Bias of Text-to-Image Generative Artificial Intelligence in Medical Imaging, Part 2: Analysis of DALL-E 3.

Journal of nuclear medicine technology
Disparity among gender and ethnicity remains an issue across medicine and health science. Only 26%-35% of trainee radiologists are female, despite more than 50% of medical students' being female. Similar gender disparities are evident across the medi...